Computing Synthetic Controls Using Bilevel Optimization

: Malo Pekka, Eskelinen Juha, Zhou Xun, Kuosmanen Timo

Publisher: Springer

: 2023

: Computational Economics

: COMPUT ECON

: 24

: 0927-7099

: 1572-9974

DOI: https://doi.org/10.1007/s10614-023-10471-7

: https://link.springer.com/article/10.1007/s10614-023-10471-7

: https://research.utu.fi/converis/portal/detail/Publication/181479863

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush-Kuhn-Tucker approximations.

KuosmanenEtAl2023ComputingSyntheticControls.pdf